--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/data_generation/mnist_resized/rescale_mnist.py	Sat Mar 19 22:51:40 2011 -0400
@@ -0,0 +1,46 @@
+import numpy,cPickle,gzip,Image,pdb,sys
+
+
+def zeropad(vect,img_size=(28,28),out_size=(32,32)):
+    delta = (numpy.abs(img_size[0]-out_size[0])/2,numpy.abs(img_size[1]-out_size[1])/2)
+    newvect = numpy.zeros(out_size)
+    newvect[delta[0]:-delta[0],delta[1]:-delta[1]] = vect.reshape(img_size)
+    return newvect.flatten()
+
+def rescale(vect,img_size=(28,28),out_size=(32,32), filter=Image.NEAREST):
+    im = Image.fromarray(numpy.asarray(vect.reshape(img_size)*255.,dtype='uint8'))
+    return (numpy.asarray(im.resize(out_size,filter),dtype='float32')/255.).flatten()
+
+ 
+#pdb.set_trace()
+def rescale_mnist(newsize=(32,32),output_file='mnist_rescaled_32_32.pkl',mnist=cPickle.load(gzip.open('mnist.pkl.gz'))):
+    newmnist = []
+    for set in mnist:
+        newset=numpy.zeros((len(set[0]),newsize[0]*newsize[1]))
+        for i in xrange(len(set[0])):
+            print i,
+            sys.stdout.flush()
+            newset[i] = rescale(set[0][i])
+        newmnist.append((newset,set[1]))
+    cPickle.dump(newmnist,open(output_file,'w'),protocol=-1)
+    print 'Done rescaling'
+
+
+def zeropad_mnist(newsize=(32,32),output_file='mnist_zeropadded_32_32.pkl',mnist=cPickle.load(gzip.open('mnist.pkl.gz'))):
+    newmnist = []
+    for set in mnist:
+        newset=numpy.zeros((len(set[0]),newsize[0]*newsize[1]))
+        for i in xrange(len(set[0])):
+            print i,
+            sys.stdout.flush()
+            newset[i] = zeropad(set[0][i])
+        newmnist.append((newset,set[1]))
+    cPickle.dump(newmnist,open(output_file,'w'),protocol=-1)
+    print 'Done padding'
+
+if __name__ =='__main__':
+    print 'Creating resized datasets'
+    mnist_ds = cPickle.load(gzip.open('mnist.pkl.gz'))
+    #zeropad_mnist(mnist=mnist_ds)
+    rescale_mnist(mnist=mnist_ds)
+    print 'Finished.'